U.S. patent number 8,341,101 [Application Number 13/461,670] was granted by the patent office on 2012-12-25 for determining relationships between data items and individuals, and dynamically calculating a metric score based on groups of characteristics.
Invention is credited to Adam Treiser.
United States Patent |
8,341,101 |
Treiser |
December 25, 2012 |
Determining relationships between data items and individuals, and
dynamically calculating a metric score based on groups of
characteristics
Abstract
Systems, apparatus, and methods for correlating two items of
interest, based on a plurality of data items and characteristics.
The data items may include objective and quantitative data, as well
as subjective and qualitative data. In one implementation, the
relationship of an individual to a metric is determined. The
system, apparatus, and methods may store characteristics describing
individuals generally, along with metrics relevant to an
organization; receive a plurality of data items; extract
information associated with the individual from the data items;
determine a number of relationships between the data items,
individuals, metric, and characteristics; and use the relationships
to determine an overall relationship between the individual and the
metric, based on the data and characteristics. In addition, related
groups of characteristics may be identified. Similarly, the
relationships between any individual, metric, sub-metric, group of
characteristics, data item, data source, characteristic, or groups
thereof may also be determined.
Inventors: |
Treiser; Adam (North Brunswick,
NJ) |
Family
ID: |
47359788 |
Appl.
No.: |
13/461,670 |
Filed: |
May 1, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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61633246 |
Feb 8, 2012 |
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Current U.S.
Class: |
706/45 |
Current CPC
Class: |
G06Q
30/02 (20130101) |
Current International
Class: |
G06F
17/00 (20060101) |
Field of
Search: |
;706/12,45,62
;705/7.29,7.37,14.4 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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Primary Examiner: Vincent; David
Attorney, Agent or Firm: Finnegan, Henderson, Farabow,
Garrett & Dunner, LLP
Claims
What is claimed is:
1. A method of identifying groups of related characteristics,
comprising: receiving, at a computer: a plurality of data items,
related to individuals; a plurality of descriptors, identifying the
individuals; a plurality of characteristics, defining categories of
the individuals; and a metric; calculating a first set of
relationships between the data items and one or more of the
individuals identified by the plurality of descriptors, wherein
each relationship comprises a magnitude and direction; calculating
a second set of relationships between the characteristics and the
data items, wherein each relationship comprises a magnitude and
direction; calculating a third set of relationships between the
metric and the characteristics, wherein each relationship comprises
a magnitude and direction; identifying groups of one or more of the
characteristics based on the second set of relationships and the
third set of relationships; dynamically calculating a metric score
based on the identified groups; and outputting the metric
score.
2. The method of claim 1, comprising: calculating a fourth set of
relationships between the groups and the characteristics, wherein
each relationship comprises a magnitude and direction; calculating
a fifth set of relationships between the groups and the metric,
based on the third and fourth relationships, wherein each
relationship comprises a magnitude and direction; representing each
relationship of the fifth set of relationships with a single
descriptor; and outputting (i) the fifth set of relationships and
(ii) the descriptors.
3. The method of claim 1, comprising: calculating a fourth set of
relationships between the groups and the characteristics, wherein
each relationship comprises a magnitude and direction; calculating
a sixth set of relationships between the individuals identified by
the plurality of descriptors and the characteristics, based on one
or more of the first set of relationships and one or more of the
second set of relationships, wherein each relationship comprises a
magnitude and direction; calculating a seventh set of relationships
between the individuals identified by the plurality of descriptors
and the groups, based on one or more of the fourth set of
relationships and one or more of the sixth set of relationships,
wherein each relationship comprises a magnitude and direction;
representing the relationships of the seventh set of relationships
with single descriptors; and outputting (i) the seventh set of
relationships and (ii) the descriptors.
4. The method of claim 1, wherein the data items comprise both
structured and unstructured information.
5. The method of claim 4, wherein the structured information
comprises quantitative information and the unstructured information
comprises qualitative information.
6. The method of claim 1, comprising: calculating an eighth set of
relationships between one or more of the plurality of groups,
wherein each relationship comprises a magnitude and direction; and
outputting the eighth set of relationships.
7. The method of claim 1, comprising: calculating a fourth set of
relationships between the groups and the characteristics, wherein
each relationship comprises a magnitude and direction; identifying
new characteristics based on: the groups; one or more of the fourth
set of relationships between the groups and the characteristics;
and one or more of the second set of relationships between the
existing characteristics and the data items; and outputting the new
characteristics.
8. The method of claim 1, wherein dynamically calculating the
metric score based on the identified groups further comprises:
calculating a fourth set of relationships between the groups and
the characteristics, wherein each relationship comprises a
magnitude and direction; calculating a fifth set of relationships
between the groups and the metric, based on one or more of the
third set of relationships and one or more of the fourth set of
relationships, wherein each relationship comprises a magnitude and
direction; receiving a plurality of sub-metrics; calculating a
ninth set of relationships between the groups and the sub-metrics,
wherein each relationship comprises a magnitude and direction;
calculating a tenth set of relationships between the sub-metrics
and the metrics, wherein each relationship comprises a magnitude
and direction; calculating a set of metric sub-scores based on one
or more of the fifth set of relationships and one or more of the
tenth set of relationships, wherein each relationship comprises a
magnitude and direction; dynamically calculating a metric score
based on one or more of the set of metric sub-scores; representing
the metric score with a single descriptor; and outputting the
descriptor.
9. The method of claim 1, wherein dynamically calculating the
metric score based on the identified groups further comprises:
receiving data items comprising both qualitative and structured
information and quantitative and unstructured information;
identifying new characteristics based on; the groups; the
relationships between the groups and the data items; and the
relationships between existing characteristics and the data items;
calculating a fourth set of relationships between the groups and
the characteristics, wherein each relationship comprises a
magnitude and direction; calculating a fifth set of relationships
between the groups and the metric based on (i) one or more or the
third set of relationships and (ii) one or more of the fourth set
of relationships, wherein each relationship comprises a magnitude
and direction; calculating a sixth set of relationships between the
individuals identified by the plurality of descriptors and the
characteristics, based on one or more of the first set of
relationships and one or more of the second set of relationships,
wherein each relationship comprises a magnitude and direction;
calculating a seventh set of relationships between the individuals
identified by the plurality of descriptors and the groups based on
one or more of the fourth set of relationships and one or more of
the sixth set of relationships, wherein each relationship comprises
a magnitude and direction; receiving a plurality of sub-metrics;
calculating a ninth set of relationships between the groups and the
sub-metrics, wherein each relationship comprises a magnitude and
direction; calculating a tenth set of relationships between the
sub-metrics and the metrics, wherein each relationship comprises a
magnitude and direction; calculating a set of metric sub-scores,
based on one or more of the fifth set of relationships and one or
more of the tenth set of relationships; dynamically calculating a
metric score based on one or more of the set of metric sub-scores;
representing the metric score with a single descriptor; and
outputting the descriptor.
10. The method of claim 9, wherein members of the first through
tenth sets of relationships comprise a direction that may be
positive, neutral, or negative.
11. A non-transitory computer-readable storage medium encoded with
instructions which, when executed on a processor, perform a method
of identifying groups of related characteristics, the method
comprising: receiving, at a computer: a plurality of data items,
related to individuals; a plurality of descriptors, identifying the
individuals; a plurality of characteristics, defining categories of
individuals; and a metric; calculating a first set of relationships
between data items and specific ones of the individuals identified
by the plurality of descriptors; calculating second set of
relationships between characteristics and data items, wherein each
relationship comprises a magnitude and direction; calculating a
third set of relationships between the metric and the
characteristics, wherein each relationship comprises a magnitude
and direction; identifying groups of one or more characteristics
based on the second set of relationships and the third set of
relationships; dynamically calculating a metric score based on the
identified groups; and outputting the metric score.
12. The storage medium of claim 11, wherein the method comprises:
calculating a fourth set of relationships between the groups and
the characteristics, wherein each relationship comprises a
magnitude and direction; calculating a fifth set of relationships
between the groups and the metric based on one or more of the third
set of relationships and one or more of the fourth set of
relationships, wherein each relationship comprises a magnitude and
direction; representing the relationships of the fifth set of
relationships with single descriptors; and outputting the fifth set
of relationships and descriptors.
13. The storage medium of claim 11, wherein the method comprises:
calculating a fourth set of relationships between the groups and
the characteristics, wherein each relationship comprises a
magnitude and direction; calculating a sixth set of relationships
between the individuals identified by the plurality of descriptors
and the characteristics, based on one or more of the first set of
relationships and one or more of the second set of relationships,
wherein each relationship comprises a magnitude and direction;
calculating a seventh set of relationships between specific ones of
the individuals identified by the plurality of descriptors and the
groups based on one or more of the fourth set of relationships and
one or more of the sixth set of relationships, wherein each
relationship comprises a magnitude and direction; representing the
relationships in the seventh set of relationships with single
descriptors; and outputting the seventh set of relationships and
descriptors.
14. The storage medium of claim 11, wherein the method comprises:
calculating an eighth set of relationships between each of the
plurality of groups, wherein each relationship comprises a
magnitude and direction; and outputting the eighth set of
relationships.
15. The storage medium of claim 11, wherein the method comprises:
calculating a fourth set of relationships between the groups and
the characteristics, wherein each relationship comprises a
magnitude and direction; identifying new characteristics based on;
the groups; one or more of the fourth set of relationships between
the groups and the characteristics; and one or more of the second
set of relationships between the existing characteristics and the
data items; and outputting the new characteristics.
16. The storage medium of claim 11, wherein dynamically calculating
a metric score based on the identified groups comprises:
calculating a fourth set of relationships between the groups and
the characteristics, wherein each relationship comprises a
magnitude and direction; calculating a fifth set of relationships
between the groups and the metric based on one or more of the third
set of relationships and one or more of the fourth set of
relationships, wherein each relationship comprises a magnitude and
direction; receiving a plurality of sub-metrics; calculating a
ninth set of relationships between the groups and the sub-metrics,
wherein each relationship comprises a magnitude and direction;
calculating a tenth set of relationships between the sub-metrics
and the metrics, wherein each relationship comprises a magnitude
and direction; calculating a set metric sub-scores, based on one or
more of the fifth set of relationships and one or more of the tenth
set of relationships; dynamically calculating a metric score based
on one or more of the set of metric sub-scores; representing the
metric score with a single descriptor; and outputting the
descriptor.
17. The storage medium of claim 11, wherein dynamically calculating
a metric score based on the identified groups comprises: receiving
data items comprising both qualitative and structured information
and quantitative and unstructured information; calculating a fourth
set of relationships between the groups and the characteristics,
wherein each relationship comprises a magnitude and direction;
identifying new characteristics based on; the groups; one or more
of the fourth set of relationships between the groups and the
characteristics; and one or more of the second set of relationships
between existing characteristics and the data items; calculating a
fifth set of relationships between the groups and the metric based
on one or more of the third set of relationships and one or more of
the fourth set of relationships, wherein each relationship
comprises a magnitude and direction; calculating a sixth set of
relationships between the individuals identified by the plurality
of descriptors and characteristics, based on one or more of the
first set of relationships and one or more of the second set of
relationships, wherein each relationship comprises a magnitude and
direction; calculating a seventh set of relationships between
specific ones of the individuals identified by the plurality of
descriptors and the groups based on one or more of the fourth set
of relationships and one or more of the sixth set of relationships,
wherein each relationship comprises a magnitude and direction;
receiving a plurality of sub-metrics; calculating a ninth set of
relationships between the groups and the sub-metrics, wherein each
relationship comprises a magnitude and direction; calculating a
tenth set of relationships between the sub-metrics and the metrics,
wherein each relationship comprises a magnitude and direction;
calculating a set of metric sub-scores, based on one or more of the
fifth set of relationships and one or more of the tenth set of
relationships, wherein each relationship comprises a magnitude and
direction; dynamically calculating a metric score, based on one or
more of the set of metric sub-scores, wherein each relationship
comprises a magnitude and direction; representing the metric score
with a single descriptor; and outputting the descriptor.
18. The storage medium of claim 17, wherein members of the first
through tenth sets of relationships comprise a direction that may
be positive, neutral, or negative.
19. A characteristic-based server to identify groups of related
characteristics of individuals, the server comprising; a data
collection module to gather data items from a plurality of data
sources; a relationship analysis module to: calculate a first set
of relationships between data items and specific ones of the
individuals identified by the plurality of descriptors, wherein
each relationship comprises a magnitude and direction; calculate a
second set of relationships between the characteristics and data
items, wherein each relationship comprises a magnitude and
direction; calculate a sixth set of relationships between the
individuals identified by the plurality of descriptors and the
characteristics, based on one or more of the first set of
relationships and one or more of the second set of relationships,
wherein each relationship comprises a magnitude and direction;
calculate a third set of relationships between the metric and the
characteristics, wherein each relationship comprises a magnitude
and direction; and dynamically calculate a metric score based on
groups of characteristics; and a grouping module to identify groups
of related characteristics based on one or more of the second set
of relationships and one or more of the third set of
relationships.
20. The server of claim 19, wherein the relationship module is
further operative to calculate: a fourth set of relationships
between the groups and the characteristics, wherein each
relationship comprises a magnitude and direction; a fifth set of
relationships between the groups and the metric based on one or
more of the third set of relationships and one or more of the
fourth set of relationships, wherein each relationship comprises a
magnitude and direction; and descriptors representing the
relationships of the fifth set of relationships.
21. The server of claim 19, wherein the relationship module is
further operative to calculate: a fourth set of relationships
between the groups and the characteristics, wherein each
relationship comprises a magnitude and direction; a sixth set of
relationships between the individuals identified by the plurality
of descriptors and the characteristics, based on one or more of the
first set of relationships and one or more of the second set of
relationships, wherein each relationship comprises a magnitude and
direction; a seventh set of relationships between specific ones of
the individuals identified by the plurality of descriptors and the
groups based on one or more of the fourth relationships and one or
more of the sixth relationships, wherein each relationship
comprises a magnitude and direction; and descriptors representing
the relationships of the seventh set of relationships.
22. The server of claim 19, wherein the relationship module is
further operative to calculate an eighth set of relationships
between each of the plurality of groups and output the
relationships, wherein each relationship comprises a magnitude and
direction.
23. The server of claim 22, wherein the characteristic-based server
further comprises: a pattern recognition module to recognize new
characteristics based on; the groups; one or more of the fourth set
of relationships between the groups and the characteristics; and
one or more of the second set of relationships between the existing
characteristics and the data items.
24. The server of claim 19, wherein the relationship module is
further operative to; calculate a fourth set of relationships
between the groups and the characteristics, wherein each
relationship comprises a magnitude and direction; calculate a fifth
set of relationships between the groups and the metric based on one
or more of the third set of relationships and one or more of the
fourth set of relationships, wherein each relationship comprises a
magnitude and direction; receive a plurality of sub-metrics;
calculate a ninth set of relationships between the groups and the
sub-metrics, wherein each relationship comprises a magnitude and
direction; calculate a tenth set of relationships between the
sub-metrics and the metrics, wherein each relationship comprises a
magnitude and direction; calculate a set of metric sub-scores,
based on one or more of the fifth set of relationships and one or
more of the tenth set of relationships; dynamically calculate an
overall metric score based on one or more of the set of metric
sub-scores; represent the overall metric score with a single
descriptor; and output the descriptor.
25. The server of claim 19, wherein; the data items comprise both
qualitative and structured information and quantitative and
unstructured information; the relationship module is further
operative to: calculate a fourth set of relationships between the
groups and the characteristics, wherein each relationship comprises
a magnitude and direction; calculate a fifth set of relationships
between the groups and the metric, based on one or more of the
third set of relationships and one or more of the fourth set of
relationships, wherein each relationship comprises a magnitude and
direction; calculate a sixth set of relationships between the
individuals identified by the plurality of descriptors and the
characteristics, based on one or more of the first set of
relationships and one or more of the second set of relationships,
wherein each relationship comprises a magnitude and direction;
calculate a seventh set of relationships between specific ones of
the individuals identified by the plurality of descriptors and the
groups, based on one or more of the fourth set of relationships and
one or more of the sixth set of relationships, wherein each
relationship comprises a magnitude and direction; receive a
plurality of sub-metrics; calculate a ninth set of relationships
between the groups and the sub-metrics, wherein each relationship
comprises a magnitude and direction; calculate a tenth set of
relationships between the sub-metrics and the metrics, wherein each
relationship comprises a magnitude and direction; calculate a set
of metric sub-scores, based on one or more of the fifth set of
relationships and one or more of the tenth set of relationships;
dynamically calculate a metric score, based on one or more of the
set of metric sub-scores; represent the metric score with a single
descriptor; and output the descriptor; and the pattern recognition
module is further operative to identify new characteristics based
on; the groups; one or more of the fourth relationships between the
groups and the characteristics; and one or more of the second
relationships between existing characteristics and the data
items.
26. The server of claim 25, wherein members of the first through
tenth sets of relationships comprise a direction that may be
positive, neutral, or negative.
Description
BACKGROUND
This Application claims the benefit of U.S. provisional Application
No. 61/633,246, entitled TOOLS AND METHODS FOR DETERMINING
RELATIONSHIP VALUES, filed Feb. 8, 2012.
1. Technical Field
The present invention relates to characteristic-based profiling
systems and, more particularly, to combining multiple points of
data regarding individuals through the use of characteristics in
order to determine the relationship between the individuals and a
user-defined criteria.
2. Description of the Related Art
Customer profiling systems are known in the art. Traditional
systems include consumer rewards cards, credit card purchase
information, demographic profiling, behavioral profiling, and
customer surveying. Some businesses supplement these traditional
systems with website and social media analytic tools that profile
the business's fans and followers according to factors such as
"likes," "click-through rates," and search engine queries, among
others. Generally, these systems attempt to determine products,
promotions, and advertisements that are most likely to appeal to a
specific customer or broad customer segment. This information helps
businesses forecast future market behavior, manage their product
portfolio and inventory levels, adjust product pricing, design
marketing strategies, and determine human resource and capital
investment needs in order to increase revenue, market share, and
profitability. For example, advertising targeted at customers who
are most likely to purchase a product may be more effective than
advertising targeting broader audiences. Likewise, products that
are related to one another are likely to be purchased by the same
customer and may sell better if offered at the same time, whether
as a package or as separate items. Online retailers often use a
similar approach, suggesting items that other customers frequently
purchase in conjunction with the selected item.
While the prior art approaches create basic customer profiles,
these profiles do not reflect the myriad similarities between
customers or the numerous ways in which customers can be grouped.
For example, the prior art approaches generally provide profiles on
either an individual customer or an overly broad customer segment
(for example, all women ages 25-34 with a college degree), failing
to reflect the various degrees of granularity with which customers
can be grouped.
One type of prior art approach typically uses only historical,
static, and quantitative or objective information. As a result,
customer profiles created by these prior art approaches are
generally outdated and inaccurate, and fail to account for the vast
amount of potentially rich, but qualitative and subjective,
information about the customer that is available to most
businesses.
A second type of prior art approach uses only subjective or
qualitative information. These approaches also have drawbacks.
Typically they use expensive and time-consuming methods such as
customer surveys or focus groups. Due to the nature of the setting,
the results may not accurately reflect the attitudes or opinions of
the surveyed individuals. Due to the expense and time involved,
only a limited number of individuals may be surveyed.
Additionally, customer information is often collected with respect
to a single business metric and may never be used to glean insights
about other metrics that may be helpful to the company. This is
particularly true for businesses that are growing and those that
have multiple departments. Growing businesses must usually adjust
or supplement their performance metrics to reflect new goals,
strategies, and business operations. As a result, these businesses
must understand how their customers relate to the new set of
business metrics rather than, or in addition to, the ones for which
the data was originally collected. Similarly, businesses with
multiple departments frequently gather customer information for
purposes of a department-specific metric, but fail to use that
information across other departments or globally within the
organization. For example, a business may have a marketing
department and risk management department. Customer information
gathered by the marketing department when researching new product
markets may never be seen or used by the risk management team to
determine whether that customer or market poses undue risk to the
business. Prior art methods for combining this disparate data, (for
example, a technique sometimes referred to as "one version of the
truth analysis") do not allow the business to apply the same method
to external data it may be interested in. Furthermore, these prior
art systems are used only to organize the information and are not
useful for analyzing it.
As a result, there is a need for a system that addresses the issues
above.
SUMMARY
In the following description, certain aspects and embodiments of
the present invention will become evident. It should be understood
that the invention, in its broadest sense, could be practiced
without having one or more features of these aspects and
embodiments. It should also be understood that these aspects and
embodiments are merely exemplary.
Consistent with an exemplary embodiment of the present invention,
there is provided a computer-readable non-transitory storage medium
having instructions which, when executed on a processor, perform a
method for identifying relationships between individuals, metrics,
and sub-metrics, using characteristics. In one embodiment, a method
of identifying related characteristics is disclosed. In this
method, a computer receives descriptions of individuals;
characteristics that define categories of individuals generally;
and a metric. The computer gathers data items and calculates a
number of relationships between the gathered data and the received
items. Based on these relationships, groups of related
characteristics can be identified, and output to a user, another
system, or stored for future use. Thus, the disclosed method can be
used to identify groups of related characteristics. In another
embodiment, instructions are contained in a non-transitory
computer-readable medium that are operable to execute the disclosed
method of identifying related characteristics. In a third
embodiment, a computer is disclosed that performs the disclosed
method of identifying related characteristics. The computer may
contain memory, a network interface, and a processor running
software operable to perform the disclosed method. It is to be
understood that both the foregoing general description and the
following detailed description are exemplary and explanatory only,
and are not restrictive of the invention, as claimed. Further
features or variations may be provided in addition to those set
forth herein. For example, the present invention may be directed to
various combinations and sub-combinations of the disclosed
features, or combinations and sub-combinations of several further
features disclosed below in the detailed descriptions.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute
a part of this specification, illustrate embodiments and together
with the description, serve to explain the principles of the
invention. In the drawings:
FIG. 1 is a block diagram of an exemplary embodiment of a
characteristic-based server;
FIG. 2 is a flowchart depicting one process for determining a
relationship score for an individual relative to a metric;
FIG. 3 is a block diagram depicting an example of relationships
between characteristics, metrics, data items, and individuals;
FIG. 4 is a block diagram depicting an example of relationships
between individuals and characteristics;
FIG. 5 is a block diagram depicting an example score for an
individual related to a metric;
FIG. 6 is a block diagram depicting an example group of
characteristics;
FIG. 7 is a block diagram depicting an example of relationships
between groups and characteristics;
FIG. 8 is a block diagram depicting an exemplary score for a
group;
FIG. 9 is a block diagram depicting relationships used to determine
sub-metric scores;
FIG. 10 is a block diagram depicting relationships used to
determine scores for a metric;
FIG. 11 is a block diagram depicting relationships between groups,
characteristics, sub-metrics, and a metric;
FIG. 12 is a block diagram depicting a sample user screen
displaying an individual and a related score;
FIG. 13 is a block diagram depicting a sample detail screen for an
individual;
FIG. 14 is a block diagram depicting a sample communication screen
for an individual;
FIG. 15 is a block diagram depicting an example of a general
notification screen.
FIG. 16 is a block diagram depicting an example of a specific
notification screen for an individual.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
Reference will now be made in detail to an exemplary embodiment of
the invention, an example of which is illustrated in the
accompanying drawings. Wherever possible, the same reference
numbers will be used throughout the drawings to refer to the same
or like parts. It is apparent, however, that the embodiments shown
in the accompanying drawings are not limiting, and that
modifications may be made without departing from the spirit and
scope of the invention.
Systems and methods consistent with the invention provide a
characteristic-based system for identifying, organizing,
describing, and visualizing relationships between a business's
metrics and individuals. To this end, the characteristic-based
system may define a number of characteristics. As used herein, the
term characteristic broadly refers to any attribute, trait, value,
or other factor associated, whether objectively or subjectively,
with an individual or group of individuals. The detailed
description below provides further examples of such
characteristics. When receiving information about an individual,
the characteristic-based system may use a suitable
relationship-determining module (i.e., a software component, a
hardware component, or a combination of a software component and a
hardware component) comprising relationship-determining algorithms
known in the art to determine the relationship between the
information and the characteristics. This relationship may be
described using both a magnitude and a direction. Further, the
description may be represented by a numerical value, textual
identifier, graphical icon, color, opacity, or any other suitable
method of representing the relationship. The magnitude may
represent how strongly the information is related to the
characteristics, including the lack of any relationship at all. The
relationship may also be identified as positive, negative, or
neutral. The term "positive" broadly refers to relationships where
the existence of, or a change in, one member of the relationship
corresponds to a similar existence of, or a similar change in, the
other members. The term "negative" broadly refers to relationships
where the existence of, or a change in one member of the
relationship corresponds to a lack of the existence of, or an
inverse change in, the other members. The term "neutral" broadly
refers to a relationship where the existence of, or a change in one
member of the relationship does not correspond to any existence or
change in the other members.
The system may also receive a plurality of descriptors, identifying
or describing specific individuals. The system may use a similar
relationship-determining module to identify which individual, or
individuals, are the most strongly related to the information.
Again, the relationships may include a magnitude, and/or a
direction identified as positive, negative, or neutral. In this
way, the system may further determine the relationship between the
individuals and the characteristics. These relationships may be
accumulated over time to develop a better understanding of the
individual, based on multiple data points.
Further, the system may use the relationship-determining modules to
identify new relationships and patterns in the data. The system may
use these relationships and patterns to create new characteristics,
which will be used when evaluating the received data. Likewise,
over time the system may identify characteristics that generally do
not relate to the data. It may flag these characteristics as
irrelevant with respect to certain data or relationships. The
system may then skip the irrelevant characteristics, increasing
performance.
The system may also use the relationship-determining module to
identify characteristics that are related to each other. The system
may group these related characteristics together, as a group of
characteristics. Any title may be given to this group of
characteristics, or to the group of individuals, data, data
sources, or metrics that have a strong relationship with that group
of characteristics. The system may use the relationship-determining
module to determine the relationships between the groups of
characteristics and the characteristics, data, individuals, and the
other groups of characteristics. In this manner, personality types
may be identified and analyzed.
In addition, the system may receive a metric, representing an
overall goal or interest of a particular organization. As used
herein, the term metric broadly refers to any attribute,
measurement, goal, strategy, or other information of interest to an
organization. The metric may also consist of a number of
sub-metrics. As used herein, the term sub-metric broadly refers to
any attribute, measurement, goal, strategy, or other information
related to the metric. The system may use a suitable
relationship-determining module to identify the relationship
between the metric and the characteristics. In this way, the system
may further determine the relationship between the metrics and the
individuals. The system may also determine the relationship between
groups of characteristics and the metric, and individuals and the
metric. In this manner, the organization may gain information on
how personality types or individuals contribute to the metric it is
interested in.
Further, a visualization module (i.e., a software component, a
hardware component, or a combination of a software component and a
hardware component) may be used to develop a representation of any
relationship or group of relationships. The user may select two
areas of interest. The selections may comprise one or more metrics,
sub-metrics, characteristics, groups of characteristics,
individuals, data items, data sources, or any grouping of the same.
Once both selections have been made, the system may use the
relationships for those selections to calculate an overall
relationship between the two. The system may then represent this
overall relationship as a single value or descriptor. Further, the
user may assign weights to one or more of the selection items, or
change the assigned weights. When the weights are changed, the
system may re-calculate all relationships and values associated
with the weights. The system may use these weights accordingly when
calculating the overall relationship between the selections. The
system may also determine the relationships between one selection
and the underlying items comprising the other selection. The system
may then compute a single value or descriptor for the underlying
items. In this manner, the user is able to determine how the
underlying items contribute to the overall relationship between the
selections.
The system may also receive a plurality of threshold criteria. As
used herein, the term threshold criteria broadly refers to any
value, term, event, or descriptor related to one or more data
items, data sources, individuals, characteristics, groups of
characteristics, or relationships. The threshold criteria may
represent a specific event, (e.g., an individual has changed their
job description), a keyword (e.g., an advertising keyword was
mentioned in a blog post), a value (e.g., a relationship is at,
above, or below the criteria), a transaction (e.g., an individual
has booked a flight), or any other criteria about which the
organization wishes to be informed. The system may output
notifications when any threshold criteria are met.
FIG. 1 is a block diagram of an exemplary embodiment of a
characteristic-based server 100. One skilled in the art will
appreciate that system 100 may be implemented in a number of
different configurations without departing from the scope of the
present invention. As shown in FIG. 1, characteristic-based system
100 may include a network interface 102, a memory module 106, a
processing module 104, a visualization module 108, and one or more
interconnected information storage units, such as, for example, a
characteristic storage unit 110, a metric storage unit 112, an
individual descriptor storage unit 114, a data item storage unit
116, a threshold criteria storage unit 118, a note storage unit
120, and a group storage unit 122. While the information storage
units in the embodiment shown in FIG. 1 are interconnected, each
information storage unit need not be interconnected. Moreover,
rather than separate storage units, characteristic-based server 100
may include only one database that would include the data of
storage units 110-122. Likewise, while the data storage units are
shown as part of server 100, in another embodiment, one or more
storage units may be separate units, connected to server 100
through network interface 102.
Network interface 102 may be one or more devices used to facilitate
the transfer of information between server 100 and external
components, such as user terminals 140, 142 and data sources 144,
146. Network interface module 102 may receive user requests from
local user terminal 140 or remote user terminal 142, and route
those requests to processing module 104 or visualization module
108. In exemplary embodiments, network interface module 102 may be
a wired or wireless interface to a local-area network connecting
one or more local user terminals 142 and local data sources 144, or
wide-area network such as the internet, connecting one or more
remote user terminals 142, or remote data sources 146. Network
interface module 102 may allow a plurality of local user terminals
140 and remote user terminals 142 to connect to the system, in
order to make selections and receive information, alerts, and
visualizations. Network interface module 102 may also allow the
system to connect to one or more local data sources 144, on a
local-area-network, or remote data sources 146, on one or more
remote networks.
Memory module 106 may represent one or more non-transitory
computer-readable storage devices that maintain information that is
used by processing module 104 and/or other components internal and
external to characteristic-based server 100. Further, memory module
106 may include one or more programs that, when executed by
processing module 104, perform one or more processes consistent
with embodiments of the present invention. Examples of such
processes are described below with respect to FIGS. 1-16. Memory
module 106 may also include configuration data that may be used by
processing module 104 to present user interface screens and
visualizations to user terminals 140 and 142. Examples of such
screens are described in greater detail with respect to FIGS.
9-16.
Processing module 104, as shown in FIG. 1, may further include a
data collection module 130, a grouping module 124, a pattern
recognition module 126, and a relationship analysis module 128.
Data collection module 130 may include components for collecting
data items from data sources, using network interface 102. As
described in more detail below, data items collected by the data
collection module may include any information pertaining to an
individual. Relationship analysis module 128 may include components
for determining the existence and strength of a relationship
between two items. For example, and as described in greater detail
below, relationship analysis module 128 may include a
natural-language processing component for determining the
relationship between two items. Grouping module 124 may include
components for identifying groups of related items. For example,
and as described in greater detail below, grouping module 124 may
use relationships identified by relationship analysis module 128 to
identify groups of related items. Pattern recognition module 126
may include components for identifying patterns in the received
data. For example, and as described in greater detail below,
pattern recognition module 126 may include pattern recognition
algorithms known in the art to identify new characteristics based
on patterns of received data.
As shown in FIG. 1, characteristic-based server 100 may also
include a plurality of interconnected storage units, 110-122. In
this regard, server 100 may include a storage unit module (not
shown) having components for controlling access to storage units
110-122. Such a storage unit module may include a query function
that, in response to a match request, may query information stored
in one or more of storage units 110-122 to identify
characteristics, data items, or metrics meeting specified criteria.
Storage units 110-122 may be configured using any appropriate type
of unit that facilitates the storage of data, as well as the
locating, accessing, and retrieving of data stored in the storage
units.
Characteristic storage unit 110 may store general characteristics
of individuals. As used herein, the term characteristic broadly
refers to any attribute, trait, value, or other factor associated,
whether objectively or subjectively, with an individual or group of
individuals. For example, a characteristic may reflect a number of
attributes that may be applicable to one or more individuals, such
as types of previously or currently held fields of work (e.g.,
salesperson), professional or personal values (e.g.,
environmentalism), location (e.g., New York), social interactions
(e.g., trendsetter), emotional traits (e.g., generally negative),
user-defined characteristics, or others.
Data item storage unit 116 may store data collected by data
collection module 130. Data item storage unit 116 may also store
metadata associated with the data items, describing the data items.
For example, metadata may include the data source the data item was
collected from, the time the data item was posted or created, the
time the data item was collected, the type of data item (e.g., a
blog post), or the individual with which the data is associated.
Data item storage unit 116 may also store data items received, or
created by characteristic-based server 100.
Metric storage unit 112 may store metrics and sub-metrics for an
organization. As used herein, a metric broadly refers to any
measurement, criteria, goal, or information of interest to an
organization. For example, a given organization may be interested
in "brand awareness," or how likely a given person is to recognize
the organization's brand. The metric may also be comprised of
sub-metrics. As used herein, a sub-metric refers to any information
related to a metric. For example, sub-metrics related to brand
awareness may include "internet mentions" for that brand, how
widely those mentions are distributed, how the mentions describe
the brand, number of sales, or others.
Individual descriptor storage unit 114 may store descriptors of
specific individuals. As used herein, an individual descriptor
includes any information that identifies a specific individual, as
opposed to a group of people. Descriptors may include names,
addresses, employee numbers, drivers license numbers, credit card
and other banking account information, social security numbers,
behavioral profiles, relationship or social network information,
linguistic styles or writing, voice recognition, image recognition
or any other unique identifiers. In this manner, each descriptor or
group of descriptors may be used to identify a unique
individual.
Threshold criteria storage unit 118 may store the threshold
criteria used to determine when a notification should occur.
Threshold criteria may include any value, term, event, or
descriptor related to one or more data items, data sources,
individuals, characteristics, groups of characteristics, or
relationships. The threshold criteria may represent a specific
event, (e.g., an individual has changed their job description), a
keyword (e.g., an advertising keyword was mentioned in a blog
post), a value (e.g., a relationship is at, above, or below the
criteria), a transaction (e.g., an individual has booked a flight),
or any other criteria about which the organization wishes to be
informed.
Note storage unit 120 may store notes, consisting of information
entered by one or more users, that are associated with one or more
individual descriptors, groups, relationships, metrics,
sub-metrics, data items, or data sources. The information may
include textual, graphical, audio, or video information. For
example, a user may enter a description of a specific group, as the
"treehugger" group. This description may allow users to more easily
refer to, and understand the characteristics that comprise that
group.
Group storage unit 122 may store groups, consisting of a plurality
of characteristics, or other groups. These groups may allow users
to more easily identify and understand categories of
individuals.
Visualization module 108, as shown in FIG. 1, may further include a
selection module 132 and a calculation module 134. Selection module
132 may include components for receiving user selections from
network interface module 102. For example, selection module 132 may
allow users on remote terminals to make selections. User selections
may consist of one or more individual descriptors, metrics,
sub-metrics, characteristics, groups, data items, data sources, or
groups thereof. Calculation module 134 may include components for
determining the relationships between the selected groups and the
remaining groups, data items, metrics, sub-metrics,
characteristics, data sources, and individuals. This may include
using the relationships to calculate an overall relationship for a
group with respect to the other groups, data items, metrics,
characteristics, data sources, and individuals. Calculation module
134 may also receive weights associated with a group, data item,
metric, sub-metric, characteristic, data source, or individual, and
use the weights in conjunction with the stored relationships when
determining the overall relationship for a selection. Visualization
module 108 may use the calculated values for a selection to build a
screen containing at least one selection, and a representation of
the overall relationship between that selection and at least one
other selection. Visualization module 108 may also include
additional information about the selection in the screen. For
example, and as discussed in more detail below, selection module
132 may receive a selection of an individual and a selection of a
metric. Calculation module 134 may determine the overall
relationship between the individual and metric based on the stored
relationships. Visualization module 108 may return a screen
containing information about the individual and a single descriptor
of the overall relationship.
Characteristic server 100 may consist of a single computer or
mainframe, containing at least a processor, memory, storage, and a
network interface. Server 100 may optionally be implemented as a
combination of instructions stored in software, executable to
perform the steps described below, and a processor connected to the
software, capable of executing the instructions. Alternatively,
server 100 may be implemented in a number of different computers,
connected to each other either through a local-area network (LAN)
or wide-area network (WAN). Data collection module 130 may
optionally comprise search engine tools known in the art, operable
to find data sources and data items relevant to the search
criteria, such as an individual. Storage units 110-122 may comprise
any computer-readable medium known in the art, including databases,
file systems, or remote servers.
FIG. 2 is a flowchart demonstrating an exemplary process 200 for
characteristic-based profiling consistent with the present
invention. For example, characteristic-based system 100 may use
process 200 to determine the relationship between an individual, or
groups of individual descriptors, and a user-specified metric based
on a number of characteristics. As shown in FIG. 2, process 200 may
begin by receiving a number of characteristics, an individual
descriptor, and a metric. A metric broadly refers to any
measurement, goal, interest, parameter, or other information that
an organization may be interested in learning.
In one embodiment, the metric will be an overall goal or
measurement related to a business. In this embodiment, the system
uses the data and characteristics to obtain information about
existing and potential customers that are positively and negatively
related to the metric. However, the system may also be used to
identify other factors related to the metric, such as
characteristics, groups of characteristics, data sources, or
sub-metrics. By recognizing new characteristics as data is
processed, the system may also identify new, previously unknown,
customers or groups of customers related to the metric. For
example, the system may use a pattern recognition module 126,
described above, to determine patterns of data that are not defined
as characteristics, but which occur on a regular basis. Once
recognized, the system may automatically define these patterns as
new characteristics.
As discussed above, characteristics broadly refer to any attribute,
trait, value, or other factor associated, whether objectively or
subjectively, with an individual or group of individuals. For
example, characteristic types may comprise: social network
(influencer, follower, etc.); sentimental (positive, neutral,
etc.); temperamental (emotionless, dramatic, etc.); attitudinal
(health conscious, eco-friendly, etc.); psychographic (personality
factors, personality-derived factors, etc.); demographic (age,
gender, etc.); transactional (past purchases, rewards, etc.);
firmographic (employment, rank, etc.); data item attributes (data
source; author, etc.); cognitive dimensions of thinking (i.e.,
evaluative, schedule-driven, etc.), or other descriptions of groups
or categories of people. A characteristic may be an objective
factor, such as age or income, a subjective factor, such as
"eco-friendly," or a combination of objective and subjective
factors. These characteristics are typically selected by a user,
based on known templates, or on the types of individuals they
believe will be relevant to one or more metrics or sub-metrics.
Alternatively, or in addition, and as described in more detail
below, the system itself may identify characteristics that are
relevant to the metric as it analyzes the data items. These
characteristics may also be obtained or purchased from other data
sources, such as marketing databases, or public websites,
discussion boards, or databases.
Individual descriptors broadly refer to any information that may be
used to identify a specific individual, including account
information, license numbers, phone numbers, email addresses, name,
relationship information, behavioral profile, nicknames or aliases,
or any information that may be used to differentiate one individual
from a group. These descriptors may be received from organizations,
users, or internal or external data sources, as described below.
Further, an individual descriptor may contain multiple pieces of
information that collectively identify a specific person. For
example, an individual descriptor may consist of a name, driver's
license number, credit card account number, and street address,
which may be used collectively to identify a specific person. This
example is not limiting, and any information that uniquely
identifies an individual may be part of an individual descriptor.
For another example, an individual descriptor may consist only of
social network information, which describes a person by their
social or business relationships to others.
At step 202, the system may receive a plurality of data items,
characteristics, sub-metrics, an individual descriptor, and a
metric. The data items may be received from a plurality of data
sources. At step 204, the system may create relevant data items for
the individual. In one embodiment, the system accesses all data
sources that may have relevant information about the metric. These
data sources may comprise internal data sources (e.g. crm, payroll,
etc.), privately-shared sources (e.g., suppliers, partners, etc.),
user-authorized data sources (e.g., social media accounts, etc.),
public data sources (e.g., blogs, tweets, etc.), or purchased data
sources (e.g., data aggregators, credit card db, etc.). As
discussed above, the purchased data sources may also contain
characteristics, metrics, or individual descriptors. In another
embodiment, the system may only access data from sources that have
been marked as relevant for one or more individual descriptors,
metrics, groups, or sub-metrics.
In general, data sources may contain both structured and
unstructured data, which may be qualitative and subjective,
quantitative and objective, or a combination of both. Structured
data broadly refers to any data that is placed into a pre-existing
structure such as a database, spreadsheet, or form. Unstructured
data broadly refers to data that does not have a defined structure,
such as prose, news articles, blog posts, comments, messages,
emoticons, images, video, audio, or other freely-entered data.
Quantitative and objective data broadly concerns factual,
measurable subjects. For example, quantitative data may be
described in terms of quantity, such as a numerical value or range.
In comparison, qualitative and subjective data broadly describes
items in terms of a quality or categorization wherein the quality
or category may not be fully defined. For example, qualitative and
subjective data may describe objects in terms of warmth and
flavor.
The system may use an appropriate relationship-determination module
(i.e., a software component, a hardware component, or a combination
of a software component and a hardware component), utilizing
techniques known in the art, to determine the strength of the
relationship between the data items and the individuals. This
relationship strength consists of a number or descriptor indicating
the magnitude of the relationship. The strength of the relationship
represents how strongly the data item is related to a specific
individual descriptor. For example, a data item discussing the
name, address, and family members of the individual would have a
strong relationship to an individual descriptor containing the same
information. Likewise, a data item that did not mention any of the
information comprising the individual descriptor would not have a
strong relationship to that descriptor. In this manner, the system
may determine which individuals are associated with the data item.
The system may also use other methods to identify the individual
associated with, or likely to be associated with a data item. For
example, the data item may be associated with a known individual
descriptor, such as a username, account, or name.
These data items will be strongly correlated with any individual
descriptor containing a matching user name, account, or name. In
another embodiment, the system may determine when the data item
refers to a pseudonym, or includes missing information about an
individual. For example, when a data item strongly relates to a
known descriptor, but the names do not match, the system may use
additional methods to determine whether the two individuals are the
same. In such a case, the system may create a pseudonym item,
containing a descriptor of the individual associated with the data
item. If additional data items are also found to have a strong
relationship to both the individual descriptor and the pseudonym,
the system may add the information from the pseudonym to the
individual descriptor. In this manner, future data items relating
to the pseudonym may be identified with the individual. If no
strong relationship is found, the system may use the pseudonym to
create a new individual descriptor.
The system may automatically use the pseudonym to create a new
individual descriptor, or add the pseudonym information to an
existing individual descriptor, if threshold relationship strengths
are met. For example, if the relationship strength between the
pseudonym and the descriptor reaches a set value, the system may
automatically merge the two. Likewise, if the relationship strength
falls below a certain threshold, the system may automatically
create a new descriptor based on the pseudonym. This behavior is
not limited to names, and the system may perform this action when
any of the information in the individual descriptor does not match
the information in the data item. In this manner, the system is
capable of collecting new information about the individuals, as
well as recognizing new individuals.
If a strong relationship exists between the data item and an
individual descriptor, the system creates an association between
the data item and the individual descriptor. The system will also
mark the data source as relevant to the individual descriptor, so
that it may be identified more quickly in the future. The system
will next use an appropriate method known in the art, such as, for
example, natural language processing, to identify the portions of
the data item that are relevant to the individual. The system uses
the relevant data portions to create a new data item, containing
only the data relevant to one or more individual descriptors. In
this embodiment, only the relevant data items will be analyzed.
At step 206, the system uses a suitable relationship-determining
module (i.e., a software component, a hardware component, or a
combination of a software component and a hardware component) to
determine the relationship between the individual descriptors and
the characteristics. The relationship-determining module may
comprise algorithms known in the art, including one or more of;
natural language processing, textual analysis, contextual analysis,
direct 1-to-1 mapping, artificial intelligence, image analysis,
speech analysis or other suitable techniques known for determining
correlations, patterns, or relationships. The relationship consists
of a magnitude, indicating the strength (or lack thereof) of the
relationship, and a direction, indicating whether the relationship
is positive, negative, or neutral. As used in this application, the
direction simply indicates whether a given relationship represents
a positive correlation (i.e. positive direction), a negative
correlation (i.e., negative direction), or no correlation (i.e.
neutral direction). For example, an individual who has repeatedly
shown "eco-friendly" behavior and attitudes will be positively
correlated with an "eco-friendly" characteristic. In this case, the
characteristic and individual descriptor would have a strong,
positive relationship. Similarly, an individual who displays
hostility towards "eco-friendly" topics and ideas would be
negatively correlated with the "eco-friendly" characteristic. The
individual descriptor for this person would have a strong negative
relationship with the "eco-friendly" characteristic. Finally, an
individual who did not correlate to the "eco-friendly"
characteristic would have a neutral relationship with it.
To determine this relationship, the system may use a
relationship-determining technique known in the art to determine
the relationship between the data items and the characteristics.
This relationship may consist of a magnitude and a direction. The
system may also calculate a value for a characteristic based on the
relationship between the characteristic and the data item, and the
relationship between the data item and the individual descriptor.
This is represented in FIG. 3, items 326-332 (first set of
relationships) and 334-340 (second set of relationships). For
example, the relationship between individual A 302 and
characteristic W 316 will be determined based on second
relationship D1W 334 and first relationship D1A 326; where second
relationship D1W 334 represents the relationship between
characteristic W and data item 1 310, and first relationship D1A
represents the relationship between data item 1 and individual A.
The combined relationships will be stored with the characteristics,
and associated with the individual descriptor as shown in FIG. 4.
The combined scores based on D1A, D1W, 402 to D1A, D1Z 408 are
associated with the relationship between individual A 302, and
characteristics W 316 to Z 322.
Returning now to FIG. 2, at step 208, the system may also determine
the relationship between the characteristics and the metric. This
relationship may also consist of a magnitude and direction, as
described above. The system may determine this relationship using a
suitable relationship-determining module (i.e., a software
component, a hardware component, or a combination of a software
component and a hardware component), known in the art. FIG. 5
illustrates an example of the third set of relationships determined
between characteristics W 316 to Z 322, and metric M 324,
represented by MW 342 to MZ 348 respectively.
At step 210, the system may determine the relationship between
individual descriptor 302 and metric 324. The system may determine
this relationship using a suitable relationship-determining module,
as described above. This relationship may also consist of a
magnitude and direction, as described above. As shown in FIG. 5,
this relationship may be determined based on the relationships
between characteristics 316-322 and metric 324, represented by MW
342 to MZ 348, and the relationships between the individual 302 and
characteristics 316 to 322, represented as D1A, D1W 402 to D1A, D1Z
408 (the sixth set of relationships).
At this point, the system may output individual-metric relationship
500, representing the strength of the relationship between
individual 302 and metric 324. This score may be represented as a
numerical value, a descriptor, an image, or any other means of
conveying the overall magnitude and/or direction of the
relationship between individual 302 and metric 324.
In another embodiment, the system may identify groups of
characteristics, in order to determine the relationship between the
groups and the metric. In this embodiment, the system may also use
a suitable relationship-determining module, as described above, to
determine the relationships between the characteristics. At step
212 in FIG. 2, the system may identify groups of characteristics
that have strong relationships to each other using grouping module
124. As shown in FIG. 6, characteristics W 316, X 318, and Z 322
are strongly related, and the system may group them into group 1
600. Because characteristic Y 320 is not strongly related to the
others, the system may not include it in group 1 600.
At step 214 in FIG. 2, the system may also determine the
relationship between the groups and the metric, based on the
underlying characteristics. For example, the system may use a
suitable relationship-determining module, as described above, to
determine the relationships between the groups and the
characteristics. For example, as shown in FIG. 7, the system
determines a fourth set of relationships G1W 702 to G1Z 708 based
on the relationship between group 1 600 and characteristics W 316
to Z 322. As described above, the relationship may contain a
magnitude and direction. As shown in FIG. 8, the system may
determine the group-metric relationship 800 (one of the fifth set
of relationships) between group 1 600 and metric 324 based on the
third set of relationship values MW 342 to MZ 348 and the fourth
set of relationships G1W 702 to G1Z 708. As described above, the
system may output group-metric relationship 800, which may be
represented as a numerical value, a descriptor, an image, or any
other means of conveying the magnitude and/or direction of the
relationship.
In yet another embodiment, the system may also determine the
relationship between the sub-metrics and the metric. For example,
at step 216, the system may also use a suitable
relationship-determining module, as described above, to determine
the relationships between the groups of characteristics and the
sub-metrics. For example, as shown in FIG. 9, the system may
determine a tenth set of relationships, the metric-sub-metric
values QM 910 to TM 916 based on the relationship between metric M
324 and sub-metrics Q 902 through T 908. The system may also
determine a ninth set of relationships, the group-sub-metric values
G1Q 918 through G1T 924, based on the relationships between group 1
600 and sub-metrics Q 902 through T 908. As described above, the
relationship may contain a magnitude and direction. The system may
also determine the overall relationship score for the sub-metrics,
based on the group-sub-metric values and metric-sub-metric values.
For example, the system may determine an overall relationship for
sub-metric Q 902 to metric M 324 based on G1Q 918 and QM 910. The
system may output this information, as described above. In this
manner, the system may determine which of the sub-metrics have the
strongest relationship to the overall metric M 324.
At step 218, the system may also determine an overall score for a
metric, representing how successful the company is in meeting its
metric, based on the collected data. For example, FIG. 10 shows an
example of overall metric score 1000, based on a plurality of
metric sub-scores, 1002-1008. The metric sub-scores are determined
based on the metric-sub-metric values 910-918, as well as the group
scores 800, 1010, 1012 for one or more groups having strong
relationships to the sub-metrics. The system may determine score
1000 for the metric based on one or more of the sub-scores
1002-1008. As described above, the system may output this score
using a suitable descriptor or value, at step 220.
FIG. 11 shows another example of the relationships between groups,
characteristics, sub-metrics, and the metric. In one embodiment,
the system may use a suitable relationship-determining module, as
described above, to determine an eighth set of relationships
between groups, represented as G12 1102, G13 1104, and G23 1106.
The system may identify groups of characteristics that have strong
relationships to each other using grouping module 124. In this
manner, the system may also create larger groups, in the event that
less granularity is desired.
It should be apparent from the above description that a similar
process may be performed starting with any metric, sub-metric, or
characteristic. For example, the system may perform a similar
process to calculate an individual score for a sub-metric with
regard to a metric. It should also be apparent that the steps may
be performed in any order, and that some steps may be omitted. It
will also be apparent to a person having skill in the art that
although the example discussed concerns business metrics and
customers, the system may be broadly used for other applications as
well. For example, an organization may have specific criteria for
suitable participants in a clinical trial. In this embodiment, the
metric would represent the criteria necessary to be a suitable
participant, and the system would allow the organization to
identify individuals who had a strong relation to the criteria.
Likewise, a metric may be an organization's performance goals for
its employees, allowing the system to identify the individual
employees with the strongest relationship to those performance
goals.
In another aspect of the system, a map of relevant data may be
built from internal data, in order to identify relevant
characteristics and data sources. For example, an organization may
already possess information about its customers or relevant
individuals. The system may analyze this data, using the steps
described above. The system may use pattern recognition module 126
to identify relevant characteristics. Once the internal data has
been processed, the system may use these characteristics when
analyzing data from external data sources. This may save time and
increase performance, since the system will use fewer irrelevant
characteristics when analyzing the new data. Additionally, in this
manner, the system may use information describing individuals it is
interested in, without revealing any of the individuals'
descriptors. This is because only characteristics, groups, or other
mapped data is used when accessing external data sources.
FIG. 12 shows an exemplary embodiment of a visualization screen for
an individual. Screen 1200 may comprise an individual descriptor
window 1202, a notification window 1204, a note window 1206, a
score window 1208, and one or more data source identifiers
1210-1214 and weight selection windows 1216-1220. Individual
descriptor window 1202 may contain information describing an
individual, based on the individual descriptor for that individual.
Notification window 1204 may display any notifications related to
the individual. Note window 1206 may display notes related to an
individual. Note window 1206 may also allow remote users to enter
notes, which will be stored and associated with the individual's
descriptor. Thus, the notes related to an individual may be entered
by a user, and associated with that user, or available to all
users. Score window 1208 may contain the overall score for the
user, relative to a metric, as computed above. Source identifiers
1210-1214 may contain icons, text, or other indicators of data
sources that have strong relationships to the individual, as
determined above. Weight selection windows 1216-1220 allow remote
users to view the current weights assigned to the data sources.
Weight selection windows 1216-1220 may also allow remote users to
enter new weights for the data sources, causing visualization
module 108 to re-calculate relationships and scores as described
above. Thus, screen 1200 allows users at remote terminals to view
information related to individuals, such as the individual's
descriptor, notes, notifications, and score. One or more of these
components may be missing, or present in a different quantity, or
different positions than shown.
FIG. 13 shows an alternative embodiment of a screen related to an
individual. Screen 1300 may comprise an individual descriptor
window 1302, notifications window 1304, note window 1306,
communication options window 1308, and data sources window 1310.
Communication options window may contain one more representations
of the preferred communications methods for the individual.
Preferred communications methods may be determined by frequency of
use, stated preferences, or weights assigned by a user. The
preferred communications window may also allow a remote user to
select a particular one of the preferred communication methods, in
order to send a message to the individual. Upon selection, the
system may present the user with a communication screen, allowing
the user to enter a message, or otherwise communicate with the
individual. One or more of these components may be missing,
duplicated, or in different positions than shown.
FIG. 14 shows exemplary communication screen 1400, allowing a
remote user to send a message to the individual. Screen 1400 may
comprise an individual descriptor window 1402, notifications window
1404, note window 1406, and message window 1408. Screen 1400 may
permit the remote user to enter a message into the message window,
or otherwise communicate with the individual. The system may send
the message to the individual, using the selected communication
medium, such as email, text message, voice message, video, or other
communication methods. Alternatively, the system may use existing
communication methods such as voice chat, video chat, instant
messaging, or phone to permit the user to communicate interactively
with the individual. One or more of these components may be
missing, duplicated, or in different positions than shown.
FIG. 15 shows exemplary notification screen 1500, allowing a remote
user to view notifications related to multiple individuals. The
screen may comprise multiple individual descriptor windows
1502-1506, and one or more threshold criteria windows 1508-1512.
Threshold criteria windows 1508-1512 may describe the criteria or
event that caused the notifications to be sent. Alternatively or
additionally, the threshold criteria windows 1508-1512 may also
display one or more data items related to the notification. One or
more of these components may be missing, duplicated, or in
different positions than shown.
FIG. 16 shows exemplary notification screen 1600 for a single
individual. The screen may comprise individual descriptor window
1602, communication options window 1604, note window 1606, data
source window 1608, threshold criteria window 1610, data item
window 1612, and score window 1614. One or more of these components
may be missing, duplicated, or in different positions than
shown.
As described above, systems and methods consistent with the
invention provide a characteristic-based system that allows an
organization to identify, organize, describe, and visualize the
relationships between individual descriptors, characteristics, and
metrics. For purposes of explanation only, certain aspects and
embodiments are described herein with reference to the components
illustrated in FIGS. 1-16. The functionality of the illustrated
components may overlap, however, and may be present in a fewer or
greater number of elements and components. Further, all or part of
the functionality of the illustrated elements may co-exist or be
distributed among several geographically dispersed locations. For
example, each "module" may be embodied as a software component, a
hardware component, or a combination of a software component and a
hardware component. Moreover, embodiments, features, aspects and
principles of the present invention may be implemented in various
environments and are not limited to the illustrated
environments.
Further, the sequences of events described in FIGS. 1-16 are
exemplary and not intended to be limiting. Thus, other process
stages may be used, and even with the processes depicted in FIGS.
1-16, the particular order of events may vary without departing
from the scope of the present invention. Moreover, certain process
stages may not be present and additional stages may be implemented
in FIGS. 1-16. Also, the processes described herein are not
inherently related to any particular system or apparatus and may be
implemented by any suitable combination of components.
Other embodiments of the invention will be apparent to those
skilled in the art from consideration of the specification and
practice of the invention disclosed herein. It is intended that the
specification and examples be considered as exemplary only, with a
true scope and spirit of the invention being indicated by the
following claims.
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